{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"09_TF_Multilayer_Perceptrons","provenance":[{"file_id":"https://github.com/madewithml/basics/blob/master/notebooks/09_Multilayer_Perceptrons.ipynb","timestamp":1582822364411}],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"eTdCMVl9YAXw","colab_type":"text"},"source":["#Multilayer Perceptron (MLP)\n","\n","In this lesson, we will explore multilayer perceptrons (MLPs) which are a basic type of neural network. We'll first motivate non-linear activation functions by trying to fit a linear model (logistic regression) on our non-linear spiral data. Then we'll implement an MLP using just NumPy and then with TensorFlow + Keras."]},{"cell_type":"markdown","metadata":{"id":"xuabAj4PYj57","colab_type":"text"},"source":["<div align=\"left\">\n","<a href=\"https://github.com/madewithml/basics/blob/master/notebooks/09_Multilayer_Perceptrons/09_TF_Multilayer_Perceptrons.ipynb\" role=\"button\"><img class=\"notebook-badge-image\" src=\"https://img.shields.io/static/v1?label=&amp;message=View%20On%20GitHub&amp;color=586069&amp;logo=github&amp;labelColor=2f363d\"></a>&nbsp;\n","<a href=\"https://colab.research.google.com/github/madewithml/basics/blob/master/notebooks/09_Multilayer_Perceptrons/09_TF_Multilayer_Perceptrons.ipynb\"><img class=\"notebook-badge-image\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"VoMq0eFRvugb","colab_type":"text"},"source":["# Overview"]},{"cell_type":"markdown","metadata":{"id":"qWro5T5qTJJL","colab_type":"text"},"source":["Our goal is to learn a model  𝑦̂   that models  𝑦  given  𝑋 . You'll notice that neural networks are just extensions of the generalized linear methods we've seen so far but with non-linear activation functions since our data will be highly non-linear.\n","\n","<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/madewithml/images/master/basics/09_Multilayer_Perceptron/nn.png\" width=\"550\">\n","</div>\n","\n","$z_1 = XW_1$\n","\n","$a_1 = f(z_1)$\n","\n","$z_2 = a_1W_2$\n","\n","$\\hat{y} = softmax(z_2)$ # classification\n","\n","* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n","* $W_1$ = 1st layer weights | $\\in \\mathbb{R}^{DXH}$ ($H$ is the number of hidden units in layer 1)\n","* $z_1$ = outputs from first layer  $\\in \\mathbb{R}^{NXH}$\n","* $f$ = non-linear activation function\n","* $a_1$ = activation applied first layer's outputs | $\\in \\mathbb{R}^{NXH}$\n","* $W_2$ = 2nd layer weights | $\\in \\mathbb{R}^{HXC}$ ($C$ is the number of classes)\n","* $z_2$ = outputs from second layer  $\\in \\mathbb{R}^{NXH}$\n","* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NXC}$ ($N$ is the number of samples)"]},{"cell_type":"markdown","metadata":{"id":"JqxyljU18hvt","colab_type":"text"},"source":["* **Objective:**  Predict the probability of class $y$ given the inputs $X$. Non-linearity is introduced to model the complex, non-linear data.\n","* **Advantages:**\n","  * Can model non-linear patterns in the data really well.\n","* **Disadvantages:**\n","  * Overfits easily.\n","  * Computationally intensive as network increases in size.\n","  * Not easily interpretable.\n","* **Miscellaneous:** Future neural network architectures that we'll see use the MLP as a modular unit for feed forward operations (affine transformation (XW) followed by a non-linear operation)."]},{"cell_type":"markdown","metadata":{"id":"3GHB1Qi3sskB","colab_type":"text"},"source":["**Note**: We're going to leave out the bias terms $\\beta$ to avoid further crowding the backpropagation calculations."]},{"cell_type":"markdown","metadata":{"id":"9vbZa-cxlujX","colab_type":"text"},"source":["# Data"]},{"cell_type":"markdown","metadata":{"id":"XtKqNioAayCy","colab_type":"text"},"source":["## Load data"]},{"cell_type":"markdown","metadata":{"id":"X3OrtMpFayFC","colab_type":"text"},"source":["Download non-linear spiral data for a classification task."]},{"cell_type":"code","metadata":{"id":"9NfIz_4OPYpG","colab_type":"code","colab":{}},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","import urllib"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ttgClvBw9aJF","colab_type":"code","colab":{}},"source":["SEED = 1234\n","DATA_FILE = \"spiral.csv\""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hBUNRWB4DIlr","colab_type":"code","colab":{}},"source":["# Set seed for reproducibility\n","np.random.seed(SEED)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"q14QCqKxUS2v","colab_type":"code","colab":{}},"source":["# Load data from GitHub to this notebook's local drive\n","url = \"https://raw.githubusercontent.com/madewithml/basics/master/data/spiral.csv\"\n","response = urllib.request.urlopen(url)\n","html = response.read()\n","with open(DATA_FILE, 'wb') as fp:\n","    fp.write(html)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"efS3lVYETA17","colab_type":"code","outputId":"a458b68a-1bcb-45e4-ef91-7f3a243593b1","executionInfo":{"status":"ok","timestamp":1583942003887,"user_tz":420,"elapsed":2539,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["# Load data\n","df = pd.read_csv(DATA_FILE, header=0)\n","X = df[['X1', 'X2']].values\n","y = df['color'].values\n","df.head(5)"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X1</th>\n","      <th>X2</th>\n","      <th>color</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.000457</td>\n","      <td>0.001951</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.001194</td>\n","      <td>0.003826</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-0.000231</td>\n","      <td>0.006008</td>\n","      <td>c1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-0.000896</td>\n","      <td>0.007966</td>\n","      <td>c1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         X1        X2 color\n","0  0.000000  0.000000    c1\n","1 -0.000457  0.001951    c1\n","2  0.001194  0.003826    c1\n","3 -0.000231  0.006008    c1\n","4 -0.000896  0.007966    c1"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"jmTwuA9kHjhA","colab_type":"code","outputId":"4c88cd5f-539a-4020-df21-ffe9ed8a4fb4","executionInfo":{"status":"ok","timestamp":1583942003887,"user_tz":420,"elapsed":2512,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["print (\"X: \", np.shape(X))\n","print (\"y: \", np.shape(y))"],"execution_count":6,"outputs":[{"output_type":"stream","text":["X:  (1500, 2)\n","y:  (1500,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jgVjStv8VnX2","colab_type":"code","outputId":"1149ea16-6eb4-4ace-8955-09141a3c1810","executionInfo":{"status":"ok","timestamp":1583942004589,"user_tz":420,"elapsed":3175,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":281}},"source":["# Visualize data\n","plt.title(\"Generated non-linear data\")\n","colors = {'c1': 'red', 'c2': 'yellow', 'c3': 'blue'}\n","plt.scatter(X[:, 0], X[:, 1], c=[colors[_y] for _y in y], edgecolors='k', s=25)\n","plt.show()"],"execution_count":7,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEICAYAAABS0fM3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUtavX4/SpV9CXNz7EGIcqlYthc8/XwwAiIu7Bk8HnmzZ5ErgCRNk1M8bN4A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SHd2nNR4O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\naMyY4WzcWOOBA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tFIj9VKj8HAZ9T2KwB7AyxvNHKk0Uiv2UyPzUaLwcC0+Hg+//zzv4po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\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NGzfYomFDWnQ6mnU6NqpR464VU9lChTgL4DsAiwH8D8DVEGfreICzAfrpdBw4cCBv377N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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"f01ldQXXS3lc","colab_type":"text"},"source":["## Split data"]},{"cell_type":"code","metadata":{"id":"38DagTg9S4ze","colab_type":"code","colab":{}},"source":["import collections\n","from sklearn.model_selection import train_test_split"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"a7HwlgP9-G52","colab_type":"code","colab":{}},"source":["TRAIN_SIZE = 0.7\n","VAL_SIZE = 0.15\n","TEST_SIZE = 0.15\n","SHUFFLE = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wRVOgVxDDcx3","colab_type":"code","colab":{}},"source":["def train_val_test_split(X, y, val_size, test_size, shuffle):\n","    \"\"\"Split data into train/val/test datasets.\n","    \"\"\"\n","    X_train, X_test, y_train, y_test = train_test_split(\n","        X, y, test_size=test_size, stratify=y, shuffle=shuffle)\n","    X_train, X_val, y_train, y_val = train_test_split(\n","        X_train, y_train, test_size=val_size, stratify=y_train, shuffle=shuffle)\n","    return X_train, X_val, X_test, y_train, y_val, y_test"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gGFqcqTDXhkl","colab_type":"code","outputId":"7d4657e3-e3e2-41fc-b4e5-c35e3382b20b","executionInfo":{"status":"ok","timestamp":1583942004591,"user_tz":420,"elapsed":3136,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X=X, y=y, val_size=VAL_SIZE, test_size=TEST_SIZE, shuffle=SHUFFLE)\n","class_counts = dict(collections.Counter(y))\n","print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n","print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n","print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n","print (f\"Sample point: {X_train[0]} → {y_train[0]}\")\n","print (f\"Classes: {class_counts}\")"],"execution_count":11,"outputs":[{"output_type":"stream","text":["X_train: (1083, 2), y_train: (1083,)\n","X_val: (192, 2), y_val: (192,)\n","X_test: (225, 2), y_test: (225,)\n","Sample point: [ 0.23623443 -0.59618506] → c1\n","Classes: {'c1': 500, 'c3': 500, 'c2': 500}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"w8EQEwO2OWfN","colab_type":"text"},"source":["## Label encoder"]},{"cell_type":"code","metadata":{"id":"0ldFOhAqObcd","colab_type":"code","colab":{}},"source":["from sklearn.preprocessing import LabelEncoder"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"mYjAYlTdObf0","colab_type":"code","colab":{}},"source":["# Output vectorizer\n","y_tokenizer = LabelEncoder()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VO1uxrdgObkf","colab_type":"code","outputId":"e31bfb90-f86e-427d-b640-49cb04835b03","executionInfo":{"status":"ok","timestamp":1583942010850,"user_tz":420,"elapsed":987,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Fit on train data\n","y_tokenizer = y_tokenizer.fit(y_train)\n","classes = list(y_tokenizer.classes_)\n","print (f\"classes: {classes}\")"],"execution_count":17,"outputs":[{"output_type":"stream","text":["classes: ['c1', 'c2', 'c3']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1t9vjih9Oeq9","colab_type":"code","outputId":"2c112909-4c99-45a3-e926-adefbe81c6ad","executionInfo":{"status":"ok","timestamp":1583942011826,"user_tz":420,"elapsed":1388,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Convert labels to tokens\n","print (f\"y_train[0]: {y_train[0]}\")\n","y_train = y_tokenizer.transform(y_train)\n","y_val = y_tokenizer.transform(y_val)\n","y_test = y_tokenizer.transform(y_test)\n","print (f\"y_train[0]: {y_train[0]}\")"],"execution_count":18,"outputs":[{"output_type":"stream","text":["y_train[0]: c1\n","y_train[0]: 0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QVgNHgSkInPE","colab_type":"code","outputId":"0e95428d-56d0-4e32-b59b-a04276c3ce9a","executionInfo":{"status":"ok","timestamp":1583942012612,"user_tz":420,"elapsed":918,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Class weights\n","counts = collections.Counter(y_train)\n","class_weights = {_class: 1.0/count for _class, count in counts.items()}\n","print (f\"class counts: {counts},\\nclass weights: {class_weights}\")"],"execution_count":19,"outputs":[{"output_type":"stream","text":["class counts: Counter({0: 361, 2: 361, 1: 361}),\n","class weights: {0: 0.002770083102493075, 2: 0.002770083102493075, 1: 0.002770083102493075}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"MDFM5gQte5rQ","colab_type":"text"},"source":["## Standardize data"]},{"cell_type":"markdown","metadata":{"id":"JlmiwCUre_ZH","colab_type":"text"},"source":["We need to standardize our data (zero mean and unit variance) in order to optimize quickly. We're only going to standardize the inputs X because out outputs y are class values."]},{"cell_type":"code","metadata":{"id":"D4uCG3vMe52J","colab_type":"code","colab":{}},"source":["from sklearn.preprocessing import StandardScaler"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3tTbwUOme5z7","colab_type":"code","colab":{}},"source":["# Standardize the data (mean=0, std=1) using training data\n","X_scaler = StandardScaler().fit(X_train)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zGTQNaRie5xb","colab_type":"code","colab":{}},"source":["# Apply scaler on training and test data (don't standardize outputs for classification)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dstH0Cm-fLgK","colab_type":"code","outputId":"3b2b1acc-2d8d-4b38-ca58-c93dc2590547","executionInfo":{"status":"ok","timestamp":1583942018079,"user_tz":420,"elapsed":1154,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["# Check (means should be ~0 and std should be ~1)\n","print (f\"X_train[0]: mean: {np.mean(X_train[:, 0], axis=0):.1f}, std: {np.std(X_train[:, 0], axis=0):.1f}\")\n","print (f\"X_train[1]: mean: {np.mean(X_train[:, 1], axis=0):.1f}, std: {np.std(X_train[:, 1], axis=0):.1f}\")\n","print (f\"X_val[0]: mean: {np.mean(X_val[:, 0], axis=0):.1f}, std: {np.std(X_val[:, 0], axis=0):.1f}\")\n","print (f\"X_val[1]: mean: {np.mean(X_val[:, 1], axis=0):.1f}, std: {np.std(X_val[:, 1], axis=0):.1f}\")\n","print (f\"X_test[0]: mean: {np.mean(X_test[:, 0], axis=0):.1f}, std: {np.std(X_test[:, 0], axis=0):.1f}\")\n","print (f\"X_test[1]: mean: {np.mean(X_test[:, 1], axis=0):.1f}, std: {np.std(X_test[:, 1], axis=0):.1f}\")"],"execution_count":23,"outputs":[{"output_type":"stream","text":["X_train[0]: mean: 0.0, std: 1.0\n","X_train[1]: mean: 0.0, std: 1.0\n","X_val[0]: mean: 0.1, std: 1.0\n","X_val[1]: mean: 0.0, std: 1.0\n","X_test[0]: mean: 0.1, std: 1.0\n","X_test[1]: mean: -0.1, std: 0.9\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IHofozO7RIiV","colab_type":"text"},"source":["# Linear model"]},{"cell_type":"markdown","metadata":{"id":"DlVmr5XkRMCf","colab_type":"text"},"source":["Before we get to our neural network, we're going to motivate non-linear activation functions by implementing a generalized linear model (logistic regression). We'll see why linear models (with linear activations) won't suffice for our dataset."]},{"cell_type":"code","metadata":{"id":"4K2qHbffAeGL","colab_type":"code","outputId":"fd275d62-c684-4ba2-d099-d400405ddab1","executionInfo":{"status":"ok","timestamp":1583942028062,"user_tz":420,"elapsed":8797,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["%tensorflow_version 2.x\n","import tensorflow as tf"],"execution_count":24,"outputs":[{"output_type":"stream","text":["TensorFlow 2.x selected.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bn9Kr2XrAeCR","colab_type":"code","colab":{}},"source":["# Set seed for reproducibility\n","tf.random.set_seed(SEED)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"5YkMN-nU-gz7","colab_type":"text"},"source":["## Model"]},{"cell_type":"code","metadata":{"id":"hMzXPa6g-467","colab_type":"code","colab":{}},"source":["from tensorflow.keras.layers import Dense\n","from tensorflow.keras.layers import Input\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.utils import plot_model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"RJlz9hEe_2xX","colab_type":"code","colab":{}},"source":["INPUT_DIM = X_train.shape[1] # X is 2-dimensional\n","HIDDEN_DIM = 100\n","NUM_CLASSES = len(classes) # 3 classes"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"oolBQZaE-43y","colab_type":"code","colab":{}},"source":["class LinearModel(Model):\n","    def __init__(self, hidden_dim, num_classes):\n","        super(LinearModel, self).__init__(name='linear_model')\n","        self.fc1 = Dense(units=hidden_dim, activation='linear', name='W1')\n","        self.fc2 = Dense(units=num_classes, activation='softmax', name='W2')\n","        \n","    def call(self, x_in, training=False):\n","        z = self.fc1(x_in)\n","        y_pred = self.fc2(z)\n","        return y_pred\n","\n","    def summary(self, input_shape):\n","        x_in = Input(shape=input_shape, name='X')\n","        summary = Model(inputs=x_in, outputs=self.call(x_in), name=self.name)\n","        return plot_model(summary, show_shapes=True) # forward pass"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"q1aORSTK-41F","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":312},"outputId":"07f8097d-afd6-4527-b003-7cc636c805a9","executionInfo":{"status":"ok","timestamp":1583942045672,"user_tz":420,"elapsed":8868,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Initialize model\n","model = LinearModel(hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)\n","model.summary(input_shape=(INPUT_DIM,))"],"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAASUAAAEnCAYAAADvr1V8AAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\nTkSuQmCC\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"AUdgkiuRBZIo","colab_type":"text"},"source":["## Training"]},{"cell_type":"code","metadata":{"id":"SiqIcs6FBo70","colab_type":"code","colab":{}},"source":["from tensorflow.keras.losses import SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UmOydQRnBYqc","colab_type":"code","colab":{}},"source":["LEARNING_RATE = 1e-2\n","NUM_EPOCHS = 10\n","BATCH_SIZE = 32"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"qg8YRdv9AZ6p","colab_type":"code","colab":{}},"source":["# Compile\n","model.compile(optimizer=Adam(lr=LEARNING_RATE),\n","              loss=SparseCategoricalCrossentropy(),\n","              metrics=[SparseCategoricalAccuracy()])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NFwrk5E2AZ2g","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":527},"outputId":"aa251ce0-63b5-4a9b-f249-1b017b5aba07","executionInfo":{"status":"ok","timestamp":1583942269724,"user_tz":420,"elapsed":4690,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Training\n","model.fit(x=X_train, \n","          y=y_train,\n","          validation_data=(X_val, y_val),\n","          epochs=NUM_EPOCHS,\n","          batch_size=BATCH_SIZE,\n","          class_weight=class_weights,\n","          shuffle=False,\n","          verbose=1)"],"execution_count":34,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:sample_weight modes were coerced from\n","  ...\n","    to  \n","  ['...']\n","WARNING:tensorflow:sample_weight modes were coerced from\n","  ...\n","    to  \n","  ['...']\n","Train on 1083 samples, validate on 192 samples\n","Epoch 1/10\n","1083/1083 [==============================] - 2s 2ms/sample - loss: 0.0022 - sparse_categorical_accuracy: 0.5199 - val_loss: 0.0021 - val_sparse_categorical_accuracy: 0.4948\n","Epoch 2/10\n","1083/1083 [==============================] - 0s 126us/sample - loss: 0.0021 - sparse_categorical_accuracy: 0.5254 - val_loss: 0.0021 - val_sparse_categorical_accuracy: 0.5052\n","Epoch 3/10\n","1083/1083 [==============================] - 0s 109us/sample - loss: 0.0021 - sparse_categorical_accuracy: 0.5263 - val_loss: 0.0021 - val_sparse_categorical_accuracy: 0.5104\n","Epoch 4/10\n","1083/1083 [==============================] - 0s 120us/sample - loss: 0.0021 - sparse_categorical_accuracy: 0.5254 - val_loss: 0.0021 - val_sparse_categorical_accuracy: 0.5104\n","Epoch 5/10\n","1083/1083 [==============================] - 0s 94us/sample - loss: 0.0021 - sparse_categorical_accuracy: 0.5254 - val_loss: 0.0021 - val_sparse_categorical_accuracy: 0.5000\n","Epoch 6/10\n","1083/1083 [==============================] - 0s 99us/sample - loss: 0.0021 - sparse_categorical_accuracy: 0.5254 - val_loss: 0.0021 - val_sparse_categorical_accuracy: 0.5000\n","Epoch 7/10\n","1083/1083 [==============================] - 0s 98us/sample - loss: 0.0021 - sparse_categorical_accuracy: 0.5235 - val_loss: 0.0021 - val_sparse_categorical_accuracy: 0.5000\n","Epoch 8/10\n","1083/1083 [==============================] - 0s 94us/sample - loss: 0.0021 - sparse_categorical_accuracy: 0.5226 - val_loss: 0.0021 - val_sparse_categorical_accuracy: 0.5000\n","Epoch 9/10\n","1083/1083 [==============================] - 0s 94us/sample - loss: 0.0021 - sparse_categorical_accuracy: 0.5235 - val_loss: 0.0021 - val_sparse_categorical_accuracy: 0.5052\n","Epoch 10/10\n","1083/1083 [==============================] - 0s 92us/sample - loss: 0.0021 - sparse_categorical_accuracy: 0.5245 - val_loss: 0.0021 - val_sparse_categorical_accuracy: 0.5052\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7f0780191710>"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"_gIcdLFeCLR_","colab_type":"text"},"source":["## Evaluation"]},{"cell_type":"code","metadata":{"id":"TyfL-k6uBzMW","colab_type":"code","colab":{}},"source":["import itertools\n","from sklearn.metrics import accuracy_score\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import confusion_matrix"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"JcfmjyWHBKeA","colab":{}},"source":["def plot_confusion_matrix(y_true, y_pred, classes, cmap=plt.cm.Blues):\n","    \"\"\"Plot a confusion matrix using ground truth and predictions.\"\"\"\n","    # Confusion matrix\n","    cm = confusion_matrix(y_true, y_pred)\n","    cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n","\n","    #  Figure\n","    fig = plt.figure()\n","    ax = fig.add_subplot(111)\n","    cax = ax.matshow(cm, cmap=plt.cm.Blues)\n","    fig.colorbar(cax)\n","\n","    # Axis\n","    plt.title(\"Confusion matrix\")\n","    plt.ylabel(\"True label\")\n","    plt.xlabel(\"Predicted label\")\n","    ax.set_xticklabels([''] + classes)\n","    ax.set_yticklabels([''] + classes)\n","    ax.xaxis.set_label_position('bottom') \n","    ax.xaxis.tick_bottom()\n","\n","    # Values\n","    thresh = cm.max() / 2.\n","    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n","        plt.text(j, i, f\"{cm[i, j]:d} ({cm_norm[i, j]*100:.1f}%)\",\n","                 horizontalalignment=\"center\",\n","                 color=\"white\" if cm[i, j] > thresh else \"black\")\n","\n","    # Display\n","    plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gdzQ8LVuGJL3","colab_type":"code","colab":{}},"source":["def plot_multiclass_decision_boundary(model, X, y, savefig_fp=None):\n","    \"\"\"Plot the multiclass decision boundary for a model that accepts 2D inputs.\n","\n","    Arguments:\n","        model {function} -- trained model with function model.predict(x_in).\n","        X {numpy.ndarray} -- 2D inputs with shape (N, 2).\n","        y {numpy.ndarray} -- 1D outputs with shape (N,).\n","    \"\"\"\n","    # Axis boundaries\n","    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n","    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n","    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101),\n","                         np.linspace(y_min, y_max, 101))\n","\n","    # Create predictions\n","    x_in = np.c_[xx.ravel(), yy.ravel()]\n","    y_pred = model.predict(x_in)\n","    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n","\n","    # Plot decision boundary\n","    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n","    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n","    plt.xlim(xx.min(), xx.max())\n","    plt.ylim(yy.min(), yy.max())\n","\n","    # Plot\n","    if savefig_fp:\n","        plt.savefig(savefig_fp, format='png')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Au0tESHKC8Rj","colab_type":"code","outputId":"3aa7fde6-a6b7-4dce-b073-92f8fef0139e","executionInfo":{"status":"ok","timestamp":1583942281489,"user_tz":420,"elapsed":1143,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Predictions\n","pred_train = model.predict(X_train) \n","pred_test = model.predict(X_test)\n","print (f\"sample probability: {pred_test[0]}\")\n","pred_train = np.argmax(pred_train, axis=1)\n","pred_test = np.argmax(pred_test, axis=1)\n","print (f\"sample class: {pred_test[0]}\")"],"execution_count":38,"outputs":[{"output_type":"stream","text":["sample probability: [0.02820205 0.13940962 0.83238834]\n","sample class: 2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"pKE1ZfJRb1b4","colab_type":"code","outputId":"e7231494-1372-403e-f1f7-ec53ae2924c4","executionInfo":{"status":"ok","timestamp":1583942283351,"user_tz":420,"elapsed":896,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Accuracy\n","train_acc = accuracy_score(y_train, pred_train)\n","test_acc = accuracy_score(y_test, pred_test)\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":39,"outputs":[{"output_type":"stream","text":["train acc: 0.53, test acc: 0.54\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"M3NVCzLWsCe3","colab_type":"code","outputId":"fa57e696-4563-41ee-b43e-8c1bcbb11a59","executionInfo":{"status":"ok","timestamp":1583942286306,"user_tz":420,"elapsed":1208,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":476}},"source":["# Metrics\n","plot_confusion_matrix(y_test, pred_test, classes=classes)\n","print (classification_report(y_test, pred_test))"],"execution_count":40,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAATgAAAEhCAYAAADrprw5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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SLJwFbgooh0ABKB82RweVpY6oSEUCckpMD2V6t27QJrLbqKsy2m/PbLTqeeEChw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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.51      0.59      0.55        75\n","           1       0.50      0.44      0.47        75\n","           2       0.60      0.59      0.59        75\n","\n","    accuracy                           0.54       225\n","   macro avg       0.54      0.54      0.54       225\n","weighted avg       0.54      0.54      0.54       225\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gGfevv1kusw8","colab_type":"code","outputId":"7ea1433a-2f2c-4390-ad98-5f5fefc5e9a0","executionInfo":{"status":"ok","timestamp":1583942288875,"user_tz":420,"elapsed":2687,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":41,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsEAAAE/CAYAAACnwR6AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCxLBy4fON+NoYWUtUvEsHr8Lt/fI9pvRaA4UhbS3T31CC2GNRqPQV/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a6rb+JuY7lssJg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wNqC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\nAAAASUVORK5CYII=\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"R3OK8p-Ng3BC","colab_type":"text"},"source":["# Activation functions"]},{"cell_type":"markdown","metadata":{"id":"ghf5uLuhg3D0","colab_type":"text"},"source":["Using the generalized linear method (logistic regression) yielded poor results because of the non-linearity present in our data yet our activation functions were linear. We need to use an activation function that can allow our model to learn and map the non-linearity in our data. There are many different options so let's explore a few."]},{"cell_type":"code","metadata":{"id":"3ZCBe8vOytx5","colab_type":"code","colab":{}},"source":["from tensorflow.keras.activations import relu\n","from tensorflow.keras.activations import sigmoid\n","from tensorflow.keras.activations import tanh"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ivnfSKEhg3Md","colab_type":"code","outputId":"bf2e0a0d-de85-4672-b951-f5a391a31950","executionInfo":{"status":"ok","timestamp":1583942294020,"user_tz":420,"elapsed":1021,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":227}},"source":["# Fig size\n","plt.figure(figsize=(12,3))\n","\n","# Data\n","x = np.arange(-5., 5., 0.1)\n","\n","# Sigmoid activation (constrain a value between 0 and 1.)\n","plt.subplot(1, 3, 1)\n","plt.title(\"Sigmoid activation\")\n","y = sigmoid(x)\n","plt.plot(x, y)\n","\n","# Tanh activation (constrain a value between -1 and 1.)\n","plt.subplot(1, 3, 2)\n","y = tanh(x)\n","plt.title(\"Tanh activation\")\n","plt.plot(x, y)\n","\n","# Relu (clip the negative values to 0)\n","plt.subplot(1, 3, 3)\n","y = relu(x)\n","plt.title(\"ReLU activation\")\n","plt.plot(x, y)\n","\n","# Show plots\n","plt.show()"],"execution_count":43,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsIAAADSCAYAAABAW6ZrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsdcBDBgwoAUObYz3VJXyGj95pdXkl1dTUF5LYXkNRZU1FFXUUlJVS/f4WH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WfiNTN+x0J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HJ+a5HV4xph24PnPdpKeU8Y/LrcBcqGmwxfC/oCSXVjJ9twy0nNK2bq/jM37SsjIKaPGHwAg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\n","text/plain":["<Figure size 864x216 with 3 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"b_yo9fwwciDY","colab_type":"text"},"source":["The ReLU activation function ($max(0,z)$) is by far the most widely used activation function for neural networks. But as you can see, each activation function has its own constraints so there are circumstances where you'll want to use different ones. For example, if we need to constrain our outputs between 0 and 1, then the sigmoid activation is the best choice."]},{"cell_type":"markdown","metadata":{"id":"u72au7H9oHk7","colab_type":"text"},"source":["**NOTE**: In some cases, using a ReLU activation function may not be sufficient. For instance, when the outputs from our neurons are mostly negative, the activation function will produce zeros. This effectively creates a \"dying ReLU\" and a recovery is unlikely. To mitigate this effect, we could lower the learning rate or use [alternative ReLU activations](https://medium.com/tinymind/a-practical-guide-to-relu-b83ca804f1f7), ex. leaky ReLU or parametric ReLU (PReLU), which have a small slope for negative neuron outputs. "]},{"cell_type":"markdown","metadata":{"id":"xqWpi0X2dWpe","colab_type":"text"},"source":["# NumPy\n","\n","Now let's create our multilayer perceptron (MLP) which is going to be exactly like the logistic regression model but with the activation function to map the non-linearity in our data. \n","\n","**NOTE**: It's normal to find the math and code in this section slightly complex. You can still read each of the steps to build intuition for when we implement this using TensorFlow + Keras.\n"]},{"cell_type":"markdown","metadata":{"id":"0prEPVOlv1F4","colab_type":"text"},"source":["Our goal is to learn a model  𝑦̂   that models  𝑦  given  𝑋 . You'll notice that neural networks are just extensions of the generalized linear methods we've seen so far but with non-linear activation functions since our data will be highly non-linear.\n","\n","$z_1 = XW_1$\n","\n","$a_1 = f(z_1)$\n","\n","$z_2 = a_1W_2$\n","\n","$\\hat{y} = softmax(z_2)$ # classification\n","\n","* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n","* $W_1$ = 1st layer weights | $\\in \\mathbb{R}^{DXH}$ ($H$ is the number of hidden units in layer 1)\n","* $z_1$ = outputs from first layer  $\\in \\mathbb{R}^{NXH}$\n","* $f$ = non-linear activation function\n","* $a_1$ = activation applied first layer's outputs | $\\in \\mathbb{R}^{NXH}$\n","* $W_2$ = 2nd layer weights | $\\in \\mathbb{R}^{HXC}$ ($C$ is the number of classes)\n","* $z_2$ = outputs from second layer  $\\in \\mathbb{R}^{NXH}$\n","* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NXC}$ ($N$ is the number of samples)"]},{"cell_type":"markdown","metadata":{"id":"pB1N3X34EHLS","colab_type":"text"},"source":["## Initialize weights"]},{"cell_type":"markdown","metadata":{"id":"a25Zg2yyvhKL","colab_type":"text"},"source":["1. Randomly initialize the model's weights $W$ (we'll cover more effective initialization strategies later in this lesson)."]},{"cell_type":"code","metadata":{"id":"Y3udiI1Idr62","colab_type":"code","outputId":"b1934329-cbf0-4d72-a83b-210d0c19395d","executionInfo":{"status":"ok","timestamp":1583942303241,"user_tz":420,"elapsed":1082,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Initialize first layer's weights\n","W1 = 0.01 * np.random.randn(INPUT_DIM, HIDDEN_DIM)\n","b1 = np.zeros((1, HIDDEN_DIM))\n","print (f\"W1: {W1.shape}\")\n","print (f\"b1: {b1.shape}\")"],"execution_count":44,"outputs":[{"output_type":"stream","text":["W1: (2, 100)\n","b1: (1, 100)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"oxF3FnErEKfr","colab_type":"text"},"source":["## Model"]},{"cell_type":"markdown","metadata":{"id":"CqllhIMgxGq7","colab_type":"text"},"source":["2. Feed inputs $X$ into the model to do the forward pass and receive the probabilities."]},{"cell_type":"markdown","metadata":{"id":"pH9-D8YE4pHH","colab_type":"text"},"source":["First we pass the inputs into the first layer.\n","  * $z_1 = XW_1$"]},{"cell_type":"code","metadata":{"id":"TAeYeoMtdr-t","colab_type":"code","outputId":"fd41c80c-4c4f-4973-a6db-b5a67c29b0e5","executionInfo":{"status":"ok","timestamp":1583942307785,"user_tz":420,"elapsed":1323,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# z1 = [NX2] · [2X100] + [1X100] = [NX100]\n","z1 = np.dot(X_train, W1) + b1\n","print (f\"z1: {z1.shape}\")"],"execution_count":45,"outputs":[{"output_type":"stream","text":["z1: (1083, 100)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"15yZEt903YOO","colab_type":"text"},"source":["Next we apply the non-linear activation function, ReLU ($max(0,z)$) in this case.\n","  * $a_1 = f(z_1)$"]},{"cell_type":"code","metadata":{"id":"yAFau-Zm3YVM","colab_type":"code","outputId":"7c60df3b-ba54-4823-cb03-17d7ff93e0df","executionInfo":{"status":"ok","timestamp":1583942308933,"user_tz":420,"elapsed":820,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Apply activation function\n","a1 = np.maximum(0, z1) # ReLU\n","print (f\"a_1: {a1.shape}\")"],"execution_count":46,"outputs":[{"output_type":"stream","text":["a_1: (1083, 100)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gNHCPVa03YGB","colab_type":"text"},"source":["We pass the activations to the second layer to get our logits.\n","  * $z_2 = a_1W_2$"]},{"cell_type":"code","metadata":{"id":"kyJS1y2o3YJb","colab_type":"code","outputId":"841213fe-0d82-48a2-cb5a-e146a41fef62","executionInfo":{"status":"ok","timestamp":1583942310737,"user_tz":420,"elapsed":1410,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Initialize second layer's weights\n","W2 = 0.01 * np.random.randn(HIDDEN_DIM, NUM_CLASSES)\n","b2 = np.zeros((1, NUM_CLASSES))\n","print (f\"W2: {W2.shape}\")\n","print (f\"b2: {b2.shape}\")"],"execution_count":47,"outputs":[{"output_type":"stream","text":["W2: (100, 3)\n","b2: (1, 3)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"k_sCtbVh5DfI","colab_type":"code","outputId":"1abadd53-57d0-47a7-8220-d5d9a40cb9bb","executionInfo":{"status":"ok","timestamp":1583942311003,"user_tz":420,"elapsed":1000,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# z2 = logits = [NX100] · [100X3] + [1X3] = [NX3]\n","logits = np.dot(a1, W2) + b2\n","print (f\"logits: {logits.shape}\")\n","print (f\"sample: {logits[0]}\")"],"execution_count":48,"outputs":[{"output_type":"stream","text":["logits: (1083, 3)\n","sample: [ 0.0004134   0.0002782  -0.00118021]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6r_NhZuP3X5g","colab_type":"text"},"source":["We'll apply the softmax function to normalize the logits and btain class probabilities.\n","  * $\\hat{y} = softmax(z_2)$"]},{"cell_type":"code","metadata":{"id":"wI3jpHGJ3X_i","colab_type":"code","outputId":"1735c779-ff14-4d1d-a473-b05b849b4b74","executionInfo":{"status":"ok","timestamp":1583942312322,"user_tz":420,"elapsed":1139,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Normalization via softmax to obtain class probabilities\n","exp_logits = np.exp(logits)\n","y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","print (f\"y_hat: {y_hat.shape}\")\n","print (f\"sample: {y_hat[0]}\")"],"execution_count":49,"outputs":[{"output_type":"stream","text":["y_hat: (1083, 3)\n","sample: [0.33352539 0.3334803  0.33299431]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"AhbLV8h7EPUT","colab_type":"text"},"source":["## Loss"]},{"cell_type":"markdown","metadata":{"id":"_VzjdcZG2h_h","colab_type":"text"},"source":["3. Compare the predictions $\\hat{y}$ (ex.  [0.3, 0.3, 0.4]]) with the actual target values $y$ (ex. class 2 would look like [0, 0, 1]) with the objective (cost) function to determine loss $J$. A common objective function for classification tasks is cross-entropy loss. \n","  * $J(\\theta) = - \\sum_i ln(\\hat{y_i}) = - \\sum_i ln (\\frac{e^{X_iW_y}}{\\sum_j e^{X_iW}}) $"]},{"cell_type":"code","metadata":{"id":"0llchiDhxGGI","colab_type":"code","colab":{}},"source":["# Loss\n","correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n","loss = np.sum(correct_class_logprobs) / len(y_train)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OsLXL8rxEYE6","colab_type":"text"},"source":["## Gradients"]},{"cell_type":"markdown","metadata":{"id":"6y4ZWFp32jd2","colab_type":"text"},"source":["4. Calculate the gradient of loss $J(\\theta)$ w.r.t to the model weights. \n","\n","The gradient of the loss w.r.t to W2 is the same as the gradients from logistic regression since $\\hat{y} = softmax(z_2)$.\n"," * $\\frac{\\partial{J}}{\\partial{W_{2j}}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2j}}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2j}}} = - \\frac{1}{\\frac{e^{W_{2y}a_1}}{\\sum_j e^{a_1W}}}\\frac{\\sum_j e^{a_1W}e^{a_1W_{2y}}0 - e^{a_1W_{2y}}e^{a_1W_{2j}}a_1}{(\\sum_j e^{a_1W})^2} = \\frac{a_1e^{a_1W_{2j}}}{\\sum_j e^{a_1W}} = a_1\\hat{y}$\n","  * $\\frac{\\partial{J}}{\\partial{W_{2y}}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2y}}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_{2y}}} = - \\frac{1}{\\frac{e^{W_{2y}a_1}}{\\sum_j e^{a_1W}}}\\frac{\\sum_j e^{a_1W}e^{a_1W_{2y}}a_1 - e^{a_1W_{2y}}e^{a_1W_{2y}}a_1}{(\\sum_j e^{a_1W})^2} = \\frac{1}{\\hat{y}}(a_1\\hat{y} - a_1\\hat{y}^2) = a_1(\\hat{y}-1)$\n","\n","  The gradient of the loss w.r.t W1 is a bit trickier since we have to backpropagate through two sets of weights.\n","  * $ \\frac{\\partial{J}}{\\partial{W_1}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}} \\frac{\\partial{\\hat{y}}}{\\partial{a_1}}  \\frac{\\partial{a_1}}{\\partial{z_1}}  \\frac{\\partial{z_1}}{\\partial{W_1}}  = W_2(\\partial{scores})(\\partial{ReLU})X $"]},{"cell_type":"code","metadata":{"id":"9D2ujwNOxGQ1","colab_type":"code","colab":{}},"source":["# dJ/dW2\n","dscores = y_hat\n","dscores[range(len(y_hat)), y_train] -= 1\n","dscores /= len(y_train)\n","dW2 = np.dot(a1.T, dscores)\n","db2 = np.sum(dscores, axis=0, keepdims=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gEpKpAgn78h4","colab_type":"code","colab":{}},"source":["# dJ/dW1\n","dhidden = np.dot(dscores, W2.T)\n","dhidden[a1 <= 0] = 0 # ReLu backprop\n","dW1 = np.dot(X_train.T, dhidden)\n","db1 = np.sum(dhidden, axis=0, keepdims=True)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tKlRXHmoEafb","colab_type":"text"},"source":["## Update weights"]},{"cell_type":"markdown","metadata":{"id":"z1j-lgGq2lb_","colab_type":"text"},"source":["5. Update the weights $W$ using a small learning rate $\\alpha$. The updates will penalize the probabiltiy for the incorrect classes (j) and encourage a higher probability for the correct class (y).\n","  * $W_i = W_i - \\alpha\\frac{\\partial{J}}{\\partial{W_i}}$"]},{"cell_type":"code","metadata":{"id":"6kZoWyx-xGW8","colab_type":"code","colab":{}},"source":["# Update weights\n","W1 += -LEARNING_RATE * dW1\n","b1 += -LEARNING_RATE * db1\n","W2 += -LEARNING_RATE * dW2\n","b2 += -LEARNING_RATE * db2"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7K8S9rf1EdHy","colab_type":"text"},"source":["## Training"]},{"cell_type":"markdown","metadata":{"id":"6lho6k572mge","colab_type":"text"},"source":["6. Repeat steps 2 - 4 until model performs well."]},{"cell_type":"code","metadata":{"id":"ScHz7h6OxGU_","colab_type":"code","outputId":"aa4d32f6-43b8-4229-b11a-eda05680b1b3","executionInfo":{"status":"ok","timestamp":1583942325438,"user_tz":420,"elapsed":5013,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# Initialize random weights\n","W1 = 0.01 * np.random.randn(INPUT_DIM, HIDDEN_DIM)\n","b1 = np.zeros((1, HIDDEN_DIM))\n","W2 = 0.01 * np.random.randn(HIDDEN_DIM, NUM_CLASSES)\n","b2 = np.zeros((1, NUM_CLASSES))\n","\n","# Training loop\n","for epoch_num in range(1000):\n","\n","    # First layer forward pass [NX2] · [2X100] = [NX100]\n","    z1 = np.dot(X_train, W1) + b1\n","\n","    # Apply activation function\n","    a1 = np.maximum(0, z1) # ReLU\n","\n","    # z2 = logits = [NX100] · [100X3] = [NX3]\n","    logits = np.dot(a1, W2) + b2\n","    \n","    # Normalization via softmax to obtain class probabilities\n","    exp_logits = np.exp(logits)\n","    y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","\n","    # Loss\n","    correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n","    loss = np.sum(correct_class_logprobs) / len(y_train)\n","\n","    # show progress\n","    if epoch_num%100 == 0:\n","        # Accuracy\n","        y_pred = np.argmax(logits, axis=1)\n","        accuracy =  np.mean(np.equal(y_train, y_pred))\n","        print (f\"Epoch: {epoch_num}, loss: {loss:.3f}, accuracy: {accuracy:.3f}\")\n","\n","    # dJ/dW2\n","    dscores = y_hat\n","    dscores[range(len(y_hat)), y_train] -= 1\n","    dscores /= len(y_train)\n","    dW2 = np.dot(a1.T, dscores)\n","    db2 = np.sum(dscores, axis=0, keepdims=True)\n","\n","    # dJ/dW1\n","    dhidden = np.dot(dscores, W2.T)\n","    dhidden[a1 <= 0] = 0 # ReLu backprop\n","    dW1 = np.dot(X_train.T, dhidden)\n","    db1 = np.sum(dhidden, axis=0, keepdims=True)\n","\n","    # Update weights\n","    W1 += -1e0 * dW1\n","    b1 += -1e0 * db1\n","    W2 += -1e0 * dW2\n","    b2 += -1e0 * db2"],"execution_count":54,"outputs":[{"output_type":"stream","text":["Epoch: 0, loss: 1.099, accuracy: 0.243\n","Epoch: 100, loss: 0.551, accuracy: 0.680\n","Epoch: 200, loss: 0.223, accuracy: 0.912\n","Epoch: 300, loss: 0.131, accuracy: 0.951\n","Epoch: 400, loss: 0.090, accuracy: 0.976\n","Epoch: 500, loss: 0.069, accuracy: 0.985\n","Epoch: 600, loss: 0.056, accuracy: 0.994\n","Epoch: 700, loss: 0.048, accuracy: 0.996\n","Epoch: 800, loss: 0.043, accuracy: 0.997\n","Epoch: 900, loss: 0.039, accuracy: 0.997\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"2hDGiO6Ng8CI","colab_type":"code","colab":{}},"source":["class MLPFromScratch():\n","    def predict(self, x):\n","        z1 = np.dot(x, W1) + b1\n","        a1 = np.maximum(0, z1)\n","        logits = np.dot(a1, W2) + b2\n","        exp_logits = np.exp(logits)\n","        y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","        return y_hat"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Dv5Y3-1ibuPM","colab_type":"code","colab":{}},"source":["# Evaluation\n","model = MLPFromScratch()\n","logits_train = model.predict(X_train)\n","pred_train = np.argmax(logits_train, axis=1)\n","logits_test = model.predict(X_test)\n","pred_test = np.argmax(logits_test, axis=1)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XfmYidNSgkcv","colab_type":"code","outputId":"a2d2b8ac-06bf-4fd7-c9df-fafd0b834271","executionInfo":{"status":"ok","timestamp":1583942328103,"user_tz":420,"elapsed":1097,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Training and test accuracy\n","train_acc =  np.mean(np.equal(y_train, pred_train))\n","test_acc = np.mean(np.equal(y_test, pred_test))\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":57,"outputs":[{"output_type":"stream","text":["train acc: 1.00, test acc: 1.00\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"I5pDl4hdgkaE","colab_type":"code","outputId":"69f8a8de-fb85-4b2d-a5a7-f4b04996dc54","executionInfo":{"status":"ok","timestamp":1583942331381,"user_tz":420,"elapsed":2698,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":58,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsEAAAE/CAYAAACnwR6AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0w6TV8xpKuloFtuPQ8gmZfiG3Bg7O1hXJHdsfhD+IQ92F8PIpRT0T/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v3Dy2maX7pTSzr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\nlFJKKaXqjgbBSimllFKq7vx/oDRSBLBSbLcAAAAASUVORK5CYII=\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"kV2rzKCLdSEu","colab_type":"text"},"source":["Credit for the plotting functions and the intuition behind all this is due to [CS231n](http://cs231n.github.io/neural-networks-case-study/), one of the best courses for machine learning. Now let's implement the MLP with TensorFlow + Keras."]},{"cell_type":"markdown","metadata":{"id":"br_O_NUCjkwm","colab_type":"text"},"source":["# TensorFlow + Keras"]},{"cell_type":"markdown","metadata":{"id":"kNwK9qm6FpVm","colab_type":"text"},"source":["## Model"]},{"cell_type":"code","metadata":{"id":"YtZvQVVpvl5p","colab_type":"code","colab":{}},"source":["class MLP(Model):\n","    def __init__(self, hidden_dim, num_classes):\n","        super(MLP, self).__init__(name='mlp')\n","        self.fc1 = Dense(units=hidden_dim, activation='relu', name='W1')\n","        self.fc2 = Dense(units=num_classes, activation='softmax', name='W2')\n","        \n","    def call(self, x_in, training=False):\n","        z = self.fc1(x_in)\n","        y_pred = self.fc2(z)\n","        return y_pred\n","\n","    def summary(self, input_shape):\n","        x_in = Input(shape=input_shape, name='X')\n","        summary = Model(inputs=x_in, outputs=self.call(x_in), name=self.name)\n","        return plot_model(summary, show_shapes=True) # forward pass"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NwnvrAUGFrZW","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":312},"outputId":"10e81fba-6d63-40c2-8863-e6c7969633e6","executionInfo":{"status":"ok","timestamp":1583942348399,"user_tz":420,"elapsed":1003,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Initialize model\n","model = MLP(hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)\n","model.summary(input_shape=(INPUT_DIM,))"],"execution_count":63,"outputs":[{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAASUAAAEnCAYAAADvr1V8AAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\n2bNnGWOM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srJS43Z0gZISMQgLCwtUV1cbOgy9aWlpAfDse3dFLBYjOTkZI0aM6FNbKSkp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\nTkSuQmCC\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":63}]},{"cell_type":"markdown","metadata":{"id":"o942ZstYFtHm","colab_type":"text"},"source":["## Training"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"nmeU45XF0O-U","colab":{}},"source":["# Compile\n","model.compile(optimizer=Adam(lr=LEARNING_RATE),\n","              loss=SparseCategoricalCrossentropy(),\n","              metrics=[SparseCategoricalAccuracy()])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"SHKHbn4xFwjl","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":547},"outputId":"2d83b45c-f280-43da-daab-15c424038106","executionInfo":{"status":"ok","timestamp":1583942356160,"user_tz":420,"elapsed":2585,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Training\n","model.fit(x=X_train, \n","          y=y_train,\n","          validation_data=(X_val, y_val),\n","          epochs=NUM_EPOCHS,\n","          batch_size=BATCH_SIZE,\n","          class_weight=class_weights,\n","          shuffle=False,\n","          verbose=1)"],"execution_count":65,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:sample_weight modes were coerced from\n","  ...\n","    to  \n","  ['...']\n","WARNING:tensorflow:sample_weight modes were coerced from\n","  ...\n","    to  \n","  ['...']\n","Train on 1083 samples, validate on 192 samples\n","Epoch 1/10\n","1083/1083 [==============================] - 0s 404us/sample - loss: 0.0021 - sparse_categorical_accuracy: 0.5439 - val_loss: 0.0017 - val_sparse_categorical_accuracy: 0.6146\n","Epoch 2/10\n","1083/1083 [==============================] - 0s 97us/sample - loss: 0.0014 - sparse_categorical_accuracy: 0.7627 - val_loss: 0.0012 - val_sparse_categorical_accuracy: 0.8490\n","Epoch 3/10\n","1083/1083 [==============================] - 0s 105us/sample - loss: 8.4931e-04 - sparse_categorical_accuracy: 0.9243 - val_loss: 7.5526e-04 - val_sparse_categorical_accuracy: 0.9167\n","Epoch 4/10\n","1083/1083 [==============================] - 0s 92us/sample - loss: 5.3142e-04 - sparse_categorical_accuracy: 0.9566 - val_loss: 5.4923e-04 - val_sparse_categorical_accuracy: 0.9635\n","Epoch 5/10\n","1083/1083 [==============================] - 0s 91us/sample - loss: 3.8045e-04 - sparse_categorical_accuracy: 0.9705 - val_loss: 4.3395e-04 - val_sparse_categorical_accuracy: 0.9635\n","Epoch 6/10\n","1083/1083 [==============================] - 0s 101us/sample - loss: 2.9891e-04 - sparse_categorical_accuracy: 0.9732 - val_loss: 3.5596e-04 - val_sparse_categorical_accuracy: 0.9792\n","Epoch 7/10\n","1083/1083 [==============================] - 0s 93us/sample - loss: 2.4744e-04 - sparse_categorical_accuracy: 0.9788 - val_loss: 2.9958e-04 - val_sparse_categorical_accuracy: 0.9844\n","Epoch 8/10\n","1083/1083 [==============================] - 0s 94us/sample - loss: 2.1195e-04 - sparse_categorical_accuracy: 0.9797 - val_loss: 2.5573e-04 - val_sparse_categorical_accuracy: 0.9948\n","Epoch 9/10\n","1083/1083 [==============================] - 0s 95us/sample - loss: 1.8555e-04 - sparse_categorical_accuracy: 0.9825 - val_loss: 2.2109e-04 - val_sparse_categorical_accuracy: 0.9948\n","Epoch 10/10\n","1083/1083 [==============================] - 0s 92us/sample - loss: 1.6559e-04 - sparse_categorical_accuracy: 0.9852 - val_loss: 1.9601e-04 - val_sparse_categorical_accuracy: 0.9948\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7f07623db8d0>"]},"metadata":{"tags":[]},"execution_count":65}]},{"cell_type":"markdown","metadata":{"id":"10HGpWGTF4oR","colab_type":"text"},"source":["## Evaluation"]},{"cell_type":"code","metadata":{"id":"Y4tI2iZ1vl8e","colab_type":"code","outputId":"afc9076f-5454-4183-d93a-4e2f05f35eb8","executionInfo":{"status":"ok","timestamp":1583942360847,"user_tz":420,"elapsed":393,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Predictions\n","pred_train = model.predict(X_train) \n","pred_test = model.predict(X_test)\n","print (f\"sample probability: {pred_test[0]}\")\n","pred_train = np.argmax(pred_train, axis=1)\n","pred_test = np.argmax(pred_test, axis=1)\n","print (f\"sample class: {pred_test[0]}\")"],"execution_count":66,"outputs":[{"output_type":"stream","text":["sample probability: [1.3245642e-03 3.8198050e-04 9.9829346e-01]\n","sample class: 2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Ieu55J1Gc7IK","colab_type":"code","outputId":"e38f1614-c0f1-4bc3-defb-5fe0cc9b3cfd","executionInfo":{"status":"ok","timestamp":1583942362365,"user_tz":420,"elapsed":1256,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Accuracy\n","train_acc = accuracy_score(y_train, pred_train)\n","test_acc = accuracy_score(y_test, pred_test)\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":67,"outputs":[{"output_type":"stream","text":["train acc: 0.99, test acc: 0.99\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"CgM4qu8I62BP","colab_type":"code","outputId":"36d74186-9d73-49d1-e533-1eee6d85972e","executionInfo":{"status":"ok","timestamp":1583942362670,"user_tz":420,"elapsed":965,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":476}},"source":["# Metrics\n","plot_confusion_matrix(y_test, pred_test, classes=classes)\n","print (classification_report(y_test, pred_test))"],"execution_count":68,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAATcAAAEhCAYAAAAaree0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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MglItIQ2AFcJSL1nOkG55F/JfCQM2+giFQGTuNolWVZAdyb7V5euIjUAL4F+olI\nsIhUwtEFzk8lIFFEygJDz9vXX0QCnHWuC/zsPPZDzvSISEMRucSD4xg/Zi23i4CqHnG2gOaISJBz\n8z9UdaeI3A8sF5FkHN3aSrkUMRKYKSLDccxs8ZCqrhGR1c5HLT513ndrDKxxthx/B+5Q1Y0iMg/Y\nAhzG8TXx/DwDrAOOOP/NXqf9QCxwKfCgqqaKyNs47sVtFMfBj2BzpF30bFYQY4xfsm6pMcYvWXAz\nxvglC27GGL9kwc0Y45csuBlj/JIFN2OMX7LgZozxS/8P26zSKEAdcE0AAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.99      0.99      0.99        75\n","           1       1.00      0.99      0.99        75\n","           2       0.99      1.00      0.99        75\n","\n","    accuracy                           0.99       225\n","   macro avg       0.99      0.99      0.99       225\n","weighted avg       0.99      0.99      0.99       225\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Xj7cwzZ4NoVI","colab_type":"code","outputId":"559dc2fa-eb10-44e3-b93b-8a011ae54471","executionInfo":{"status":"ok","timestamp":1583942365381,"user_tz":420,"elapsed":2154,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":69,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAsEAAAE/CAYAAACnwR6AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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1wA8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\nRU5ErkJggg==\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"6Cv3ev9Uo1Ii","colab_type":"text"},"source":["## Inference"]},{"cell_type":"code","metadata":{"id":"h25pUbR8o9eF","colab_type":"code","outputId":"eb9de186-e17a-45fe-d833-f0f22edb13f5","executionInfo":{"status":"ok","timestamp":1583942367300,"user_tz":420,"elapsed":614,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":80}},"source":["# Inputs for inference\n","X_infer = pd.DataFrame([{'X1': 0.1, 'X2': 0.1}])\n","X_infer.head()"],"execution_count":70,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X1</th>\n","      <th>X2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.1</td>\n","      <td>0.1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    X1   X2\n","0  0.1  0.1"]},"metadata":{"tags":[]},"execution_count":70}]},{"cell_type":"code","metadata":{"id":"8G5lIYReo9l_","colab_type":"code","outputId":"011faa32-de07-4e4d-ef3e-ccc9656c7324","executionInfo":{"status":"ok","timestamp":1583942368450,"user_tz":420,"elapsed":1223,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Standardize\n","X_infer = X_scaler.transform(X_infer)\n","print (X_infer)"],"execution_count":71,"outputs":[{"output_type":"stream","text":["[[0.30945845 0.30761858]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Uow5g2-1o9kF","colab_type":"code","outputId":"6dd51f46-6f22-493b-e84f-01081c8d27c9","executionInfo":{"status":"ok","timestamp":1583942368836,"user_tz":420,"elapsed":1009,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Predict\n","y_infer = model.predict(X_infer)\n","_class = np.argmax(y_infer)\n","print (f\"The probability that you have a class {classes[_class]} is {y_infer[0][_class]*100.0:.0f}%\")"],"execution_count":72,"outputs":[{"output_type":"stream","text":["The probability that you have a class c1 is 97%\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gWv-RU46k2UL","colab_type":"text"},"source":["# Initializing weights"]},{"cell_type":"markdown","metadata":{"id":"1hDPBE0sk2mJ","colab_type":"text"},"source":["So far we have been initializing weights with small random values and this isn't optimal for convergence during training. The objective is to have weights that are able to produce outputs that follow a similar distribution across all neurons. We can do this by enforcing weights to have unit variance prior the affine and non-linear operations."]},{"cell_type":"markdown","metadata":{"id":"YxCm7FyRFRWK","colab_type":"text"},"source":["**NOTE**: A popular method is to apply [xavier initialization](http://andyljones.tumblr.com/post/110998971763/an-explanation-of-xavier-initialization), which essentially initializes the weights to allow the signal from the data to reach deep into the network. You may be wondering why we don't do this for every forward pass and that's a great question. We'll look at more advanced strategies that help with optimization like batch/layer normalization, etc. in future lessons. Meanwhile you can check out other initializers [here](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/initializers)."]},{"cell_type":"code","metadata":{"id":"B7l7tg7SIgPF","colab_type":"code","colab":{}},"source":["from tensorflow.keras.initializers import glorot_normal"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"U5lUR__TKvqA","colab_type":"code","colab":{}},"source":["# Dense layer with glorot initializer\n","z = Dense(HIDDEN_DIM, activation='relu', kernel_initializer=glorot_normal(), name='W1') # xavier glorot initiailization"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FAyjh3bieLjn","colab_type":"text"},"source":["# Dropout"]},{"cell_type":"markdown","metadata":{"id":"z6kLwcBveLy5","colab_type":"text"},"source":["A great technique to have our models generalize (perform well on test data) is to increase the size of your data but this isn't always an option. Fortuntely, there are methods like regularization and dropout that can help create a more robust model. \n","\n","Dropout is a technique (used only during training) that allows us to zero the outputs of neurons. We do this for `dropout_p`% of the total neurons in each layer and it changes every batch. Dropout prevents units from co-adapting too much to the data and acts as a sampling strategy since we drop a different set of neurons each time.\n","\n","<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/madewithml/images/master/basics/09_Multilayer_Perceptron/dropout.png\" width=\"350\">\n","</div>\n","\n","* [Dropout: A Simple Way to Prevent Neural Networks from\n","Overfitting](http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf)"]},{"cell_type":"code","metadata":{"id":"HT75Tnn-EVBM","colab_type":"code","colab":{}},"source":["from tensorflow.keras.layers import Dropout\n","from tensorflow.keras.regularizers import l2"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"g1E2bDj2HKpb","colab_type":"code","colab":{}},"source":["DROPOUT_P = 0.1 # % of the neurons that are dropped each pass\n","LAMBDA_L2 = 1e-4 # L2 regularization"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DS26SgeFgvG1","colab_type":"code","colab":{}},"source":["class MLPWithDropout(Model):\n","    def __init__(self, hidden_dim, lambda_l2, dropout_p, num_classes):\n","        super(MLPWithDropout, self).__init__(name='mlp_with_dropout')\n","        self.fc1 = Dense(units=hidden_dim, activation='relu', \n","                         kernel_initializer=glorot_normal(), \n","                         kernel_regularizer=l2(lambda_l2), # adding L2 regularization\n","                         name='W1')\n","        self.dropout = Dropout(rate=dropout_p, name='dropout')\n","        self.fc2 = Dense(units=num_classes, activation='softmax', name='W2')\n","        \n","    def call(self, x_in, training=False):\n","        z = self.fc1(x_in)\n","        z = self.dropout(z, training=training)\n","        y_pred = self.fc2(z)\n","        return y_pred\n","    \n","    def summary(self, input_shape):\n","        x_in = Input(shape=input_shape, name='X')\n","        summary = Model(inputs=x_in, outputs=self.call(x_in), name=self.name)\n","        return plot_model(summary, show_shapes=True) # forward pass"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1he_g3eJgvOk","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":422},"outputId":"26288aa4-52e0-44d6-de59-ac48d4085deb","executionInfo":{"status":"ok","timestamp":1583942387459,"user_tz":420,"elapsed":1647,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Initialize model\n","model = MLPWithDropout(hidden_dim=HIDDEN_DIM, lambda_l2=LAMBDA_L2, \n","                       dropout_p=DROPOUT_P, num_classes=NUM_CLASSES)\n","model.summary(input_shape=(INPUT_DIM,))"],"execution_count":79,"outputs":[{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAUkAAAGVCAIAAAD8FJSrAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\nL/2vt6cO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xsbGBw8eXLJkSXV1tWxNATAR6hLbCCEej/fgwYO1a9eKFwYHB1dXV/v7+4+r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\n9P8At7IuThmZBUIAAAAASUVORK5CYII=\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":79}]},{"cell_type":"markdown","metadata":{"id":"9ijvHwcZg8mO","colab_type":"text"},"source":["# Overfitting"]},{"cell_type":"markdown","metadata":{"id":"FIhvdD_zg8os","colab_type":"text"},"source":["Though neural networks are great at capturing non-linear relationships they are highly susceptible to overfitting to the training data and failing to generalize on test data. Just take a look at the example below where we generate completely random data and are able to fit a model with [$2*N*C + D$](https://arxiv.org/abs/1611.03530) hidden units. The training performance is good (~70%) but the overfitting leads to very poor test performance. We'll be covering strategies to tackle overfitting in future lessons."]},{"cell_type":"code","metadata":{"id":"uRdM16NhazJP","colab_type":"code","colab":{}},"source":["NUM_EPOCHS = 500\n","NUM_SAMPLES_PER_CLASS = 50\n","LEARNING_RATE = 1e-1\n","HIDDEN_DIM = 2 * NUM_SAMPLES_PER_CLASS * NUM_CLASSES + INPUT_DIM # 2*N*C + D"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"qf00Biq6g8ty","colab_type":"code","outputId":"668370b0-e69e-4e42-9fa5-410486968fee","executionInfo":{"status":"ok","timestamp":1583942394665,"user_tz":420,"elapsed":1653,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Generate random data\n","X = np.random.rand(NUM_SAMPLES_PER_CLASS * NUM_CLASSES, INPUT_DIM)\n","y = np.array([[i]*NUM_SAMPLES_PER_CLASS for i in range(NUM_CLASSES)]).reshape(-1)\n","print (\"X: \", format(np.shape(X)))\n","print (\"y: \", format(np.shape(y)))"],"execution_count":81,"outputs":[{"output_type":"stream","text":["X:  (150, 2)\n","y:  (150,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-bA9vK9SWkjh","colab_type":"code","outputId":"52685867-2c6c-46eb-ecf8-5524a76cbc96","executionInfo":{"status":"ok","timestamp":1583942395044,"user_tz":420,"elapsed":1588,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X, y, val_size=VAL_SIZE, test_size=TEST_SIZE, shuffle=SHUFFLE)\n","print (\"X_train:\", X_train.shape)\n","print (\"y_train:\", y_train.shape)\n","print (\"X_val:\", X_val.shape)\n","print (\"y_val:\", y_val.shape)\n","print (\"X_test:\", X_test.shape)\n","print (\"y_test:\", y_test.shape)"],"execution_count":82,"outputs":[{"output_type":"stream","text":["X_train: (107, 2)\n","y_train: (107,)\n","X_val: (20, 2)\n","y_val: (20,)\n","X_test: (23, 2)\n","y_test: (23,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"lYwc9mk1f5bm","colab_type":"code","colab":{}},"source":["# Standardize the inputs (mean=0, std=1) using training data\n","X_scaler = StandardScaler().fit(X_train)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"xozz2bBoWkmq","colab_type":"code","colab":{}},"source":["class OverfitMLP(Model):\n","    def __init__(self, hidden_dim, num_classes):\n","        super(OverfitMLP, self).__init__(name='overfit_mlp')\n","        self.fc1 = Dense(units=hidden_dim, activation='relu', name='W1')\n","        self.fc2 = Dense(units=num_classes, activation='softmax', name='W2')\n","        \n","    def call(self, x_in, training=False):\n","        z = self.fc1(x_in)\n","        y_pred = self.fc2(z)\n","        return y_pred\n","    \n","    def summary(self, input_shape):\n","        x_in = Input(shape=input_shape, name='X')\n","        summary = Model(inputs=x_in, outputs=self.call(x_in), name=self.name)\n","        return plot_model(summary, show_shapes=True) # forward pass"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DDUW0xVMh8v8","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":312},"outputId":"55e45e58-4259-4fd1-85d4-9f9ce9a55045","executionInfo":{"status":"ok","timestamp":1583942404711,"user_tz":420,"elapsed":1146,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Initialize model\n","model = OverfitMLP(hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)\n","model.summary(input_shape=(INPUT_DIM,))"],"execution_count":87,"outputs":[{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAASUAAAEnCAYAAADvr1V8AAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\ngg==\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":87}]},{"cell_type":"code","metadata":{"id":"bXnkWoPaWkpe","colab_type":"code","colab":{}},"source":["# Compile\n","optimizer = Adam(lr=LEARNING_RATE)\n","model.compile(optimizer=optimizer,\n","              loss=SparseCategoricalCrossentropy(),\n","              metrics=[SparseCategoricalAccuracy()])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zgayj4E1WksH","colab_type":"code","colab":{}},"source":["# Training\n","model.fit(x=X_train, \n","          y=y_train,\n","          validation_data=(X_val, y_val),\n","          epochs=NUM_EPOCHS,\n","          batch_size=BATCH_SIZE,\n","          class_weight=class_weights,\n","          shuffle=False,\n","          verbose=1)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"p3OJLNwuZxtk","colab_type":"code","outputId":"06b93b67-fb5d-4494-b5a5-b44bddfcd615","executionInfo":{"status":"ok","timestamp":1583942420125,"user_tz":420,"elapsed":13053,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Predictions\n","pred_train = model.predict(X_train) \n","pred_test = model.predict(X_test)\n","print (f\"sample probability: {pred_test[0]}\")\n","pred_train = np.argmax(pred_train, axis=1)\n","pred_test = np.argmax(pred_test, axis=1)\n","print (f\"sample class: {pred_test[0]}\")"],"execution_count":90,"outputs":[{"output_type":"stream","text":["sample probability: [0.01885148 0.5590972  0.4220513 ]\n","sample class: 1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bDoiOTgxdgLd","colab_type":"code","outputId":"da097462-24d5-4ab5-eac2-3a2b191a5b0a","executionInfo":{"status":"ok","timestamp":1583942420126,"user_tz":420,"elapsed":11738,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Accuracy\n","train_acc = accuracy_score(y_train, pred_train)\n","test_acc = accuracy_score(y_test, pred_test)\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":91,"outputs":[{"output_type":"stream","text":["train acc: 0.79, test acc: 0.22\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_LU9Wzt0ZxwI","colab_type":"code","outputId":"324efa04-1679-41ce-e110-b37db5c19b0e","executionInfo":{"status":"ok","timestamp":1583942421704,"user_tz":420,"elapsed":1556,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":476}},"source":["# Classification report\n","plot_confusion_matrix(y_true=y_test, y_pred=pred_test, classes=classes)\n","print (classification_report(y_test, pred_test))"],"execution_count":92,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAUEAAAEhCAYAAADh+hJrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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bZlczZu3MBtt98JwMjb7qBf397s2L6d0/v05d13/sett91ebN/uPXqyaOEP5T+QQPAzAAZj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\nAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.25      0.25      0.25         8\n","           1       0.25      0.25      0.25         8\n","           2       0.14      0.14      0.14         7\n","\n","    accuracy                           0.22        23\n","   macro avg       0.21      0.21      0.21        23\n","weighted avg       0.22      0.22      0.22        23\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rpSoAEdGWku5","colab_type":"code","outputId":"e20c489f-eeb8-46c0-fb21-690e3744a738","executionInfo":{"status":"ok","timestamp":1583942423342,"user_tz":420,"elapsed":3160,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":93,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAssAAAE/CAYAAACw445JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKon58PdtCTG3gLVcal1Hap3G4Mx4PsNKQkqJ/7SfqRMz6GEdYRN4N9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pKF05/n5tDBKB7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\nb2PDzh0YiSSa05Fz5rWQ1LY0obtcGIn5VnBC12m6sfRvNGux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Dlz8r3F2oezEdnCDYz1c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\nlCrg/wPQQFiGXqQcPwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"yppKnFIL6Vv7","colab_type":"text"},"source":["It's important that we experiment, starting with simple models that underfit (high bias) and improve it towards a good fit. Starting with simple models (linear/logistic regression) let's us catch errors without the added complexity of more sophisticated models (neural networks). "]},{"cell_type":"markdown","metadata":{"id":"WC0m-lMnE4Jp","colab_type":"text"},"source":["<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/madewithml/images/master/basics/09_Multilayer_Perceptron/fit.png\" width=\"700\">\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"xk5RXmKtFfD1","colab_type":"text"},"source":["---\n","Share and discover ML projects at <a href=\"https://madewithml.com/\">Made With ML</a>.\n","\n","<div align=\"left\">\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://github.com/madewithml/basics\"><img src=\"https://img.shields.io/github/stars/madewithml/basics.svg?style=social&label=Star\"></a>&nbsp;\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://www.linkedin.com/company/madewithml\"><img src=\"https://img.shields.io/badge/style--5eba00.svg?label=LinkedIn&logo=linkedin&style=social\"></a>&nbsp;\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://twitter.com/madewithml\"><img src=\"https://img.shields.io/twitter/follow/madewithml.svg?label=Follow&style=social\"></a>\n","</div>\n","             "]}]}